Program Behavior: Models and Measurements
Program Behavior: Models and Measurements
Synthesis and Optimization of Digital Circuits
Synthesis and Optimization of Digital Circuits
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Dynamic Programming
Performance analysis through synthetic trace generation
ISPASS '00 Proceedings of the 2000 IEEE International Symposium on Performance Analysis of Systems and Software
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The controls for reconfigurable manufacturing systems RMS have to be capable not only of identifying exceptions on-line, but also simultaneously developing on-line strategies for unpredictable customer orders or inaccurate estimate of processing times. Trace-driven simulators are an efficient alternative but maintaining large traces can present storage and portability problems. This paper proposes a distribution-driven trace generation methodology as an alternative to traditional trace-driven simulation. An adaptation of the Least Recently Used Stack Model is used to concisely capture the key locality features in a trace and a two-position Markov chain model is used for trace generation. Simulation and analysis of a variety of RMS application traces demonstrate the characteristics of the synthetic traces should be generally very well preserved and similar to their real trace, and we also highlight the potential performance improvement over Tracking Control-Logic.